diff --git a/malaria.py b/malaria.py
index 09e1fa5..010bdde 100644
--- a/malaria.py
+++ b/malaria.py
@@ -8,7 +8,7 @@ from enum import IntEnum
 class Model:
     def __init__(self, width=32, height=32, humandens=0.15, mosquitodens=0.10,
                  immunepct=0.1, mosqinfpct=0.1, hm_infpct=0.5, mh_infpct=0.5,
-                 hinfdiepct=0.01, mhungrypct=0.1, humandiepct=10**-3,
+                 hinfdiepct=0.01, mhungrypct=0.1, humandiepct=10**-6,
                  mosqdiepct=10**-3, mosqnetdens=0.05, time_steps=2000,
                  graphical=True):
         
@@ -25,9 +25,9 @@ class Model:
         self.immunepct = immunepct
         # Chance for a mosquito to be infectuous
         self.mosqinfpct = mosqinfpct
-        # Chance for a human to be infected by a mosquito bite
+        # Chance for a mosquito to be infected by a human
         self.hm_infpct = hm_infpct
-        # Chance for a mosquito to be infected from biting an infected human
+        # Chance for a human to be infected from a mosquito
         self.mh_infpct = mh_infpct
         # Chance that an infected human dies
         self.hinfdiepct = hinfdiepct
@@ -46,6 +46,17 @@ class Model:
         self.mosquitos = self.gen_mosquitos()
         self.nets = self.gen_nets()
 
+        # statistics
+        self.stats = {
+            "natural deaths": 0,
+            "malaria deaths": 0,
+            "total deaths": 0,
+            "mosquitos fed": 0,
+            "humans infected": 0,
+            "mosquitos infected": 0,
+            "net count": 0
+        }
+
         if self.graphical:
             self.init_draw()
        
@@ -57,6 +68,9 @@ class Model:
         self.norm = matplotlib.colors.BoundaryNorm(bounds, self.colors.N)
 
     def recycle_human(self):
+        """
+        Determine if a human dies of natural causes and then replace them by a new human
+        """
         # Get all living humans
         humans = np.transpose(np.where(self.grid != Human.DEAD))
 
@@ -71,6 +85,7 @@ class Model:
 
         death_count = len(humans) - humans_survive
 
+        self.stats["natural deaths"] += death_count
 
         # Pick a random, unpopulated spot
         births = np.array(random.sample(list(np.transpose(np.where(self.grid == Human.DEAD))),
@@ -93,8 +108,40 @@ class Model:
 
         # Now let's kill them
         self.grid[infected[deaths][:, 0], infected[deaths][:, 1]] = Human.DEAD
+
+        self.stats["malaria deaths"] += len(np.where(deaths)[0])
+
+    def feed(self):
+        #TODO: dit refactoren?
+        """
+        Feed the mosquitos that want to and can be fed
+        """
+        for mos in self.mosquitos:
+            if not mos.hungry:
+                continue
+            # state of current place on the grid where mosquito lives
+            state = self.grid[mos.x, mos.y]
+
+            if state != Human.DEAD:
+                self.stats["mosquitos fed"] += 1
+                mos.hungry = False
+                
+                # check if healthy human needs to be infected or mosquito becomes infected from eating
+                if state == Human.HEALTHY and mos.infected and random.uniform(0, 1) < self.mh_infpct:
+                    self.grid[mos.x, mos.y] = Human.INFECTED
+                    self.stats["humans infected"] += 1
+                elif state == Human.INFECTED and not mos.infected and random.uniform(0, 1) < self.hm_infpct:
+                    self.stats["mosquitos infected"] += 1
+                    mos.infected = True
+
+    def determine_hunger(self):
+        """
+        Determines which mosquitos should get hungry
+        """
+        for mos in self.mosquitos:
+            mos.hungry = not mos.hungry and random.uniform(0, 1) < self.mhungrypct
     
-    def get_movementbox(self, x, y):
+    def get_movementbox(self, x: int, y: int):
         """
         Returns indices of a moore neighbourhood around the given index
         """
@@ -176,17 +223,36 @@ class Model:
                                 p=[1-self.mosqnetdens, self.mosqnetdens],
                                 size=(self.width, self.height))
 
-
     def run(self):
         """
         This functions runs the simulation
         """
+        print(chr(27) + "[2J")
         for t in range(self.time_steps):
+            print("Simulating timestep: {}".format(t), end='\r')
             self.step()
             if self.graphical:
                 self.draw(t)
-            else:
-                print("Simulating timestep: {}".format(t), end='\r')
+            
+        print(chr(27) + "[2J")
+        self.compile_stats()
+        self.print_stats()
+
+    def compile_stats(self):
+        """
+        Compiles a comprehensive list of statistics of the simulation
+        """
+        self.stats["total deaths"] = self.stats["malaria deaths"] + self.stats["natural deaths"]
+       
+        # print(np.where(self.nets))
+        self.stats["net count"] = len(np.where(self.nets)[0])
+
+    def print_stats(self):
+        """
+        Prints the gathered statistics from the simulation
+        """
+        for stat in self.stats:
+            print(f"{stat}: {self.stats[stat]}")
 
     def step(self):
         """
@@ -198,9 +264,15 @@ class Model:
         self.recycle_human()
         # move mosquitos
         self.move_mosquitos()
+        # feed hungry mosquitos
+        self.feed()
+        # make mosquitos hungry again
+        self.determine_hunger()
         
-
     def draw(self, t: int):
+        """
+        Draws the grid of humans, tents and mosquitos
+        """
         # this function draws the humans
         plt.title("t={}".format(t))
         # draw the grid
@@ -240,9 +312,9 @@ class Human(IntEnum):
     HEALTHY = 1
     INFECTED = 2
     IMMUNE = 3
-    
+
 
 if __name__ == "__main__":
-    model = Model(graphical=True)
+    model = Model(graphical=False)
     model.run()